The high penetration of renewable energy in electricity grids brings the role of balancing production and demand for Solar Thermal Energy (STE) plants. Due to their thermal storage capability, STE plants provide certain flexibility between collecting thermal energy from the sun and transforming it into electric energy by means of a power cycle. The dispatchability of STE plants is based on the electricity demand and weather forecasts, to accurately schedule the electricity production over the following days. Such scheduling enables the participation of STE plants in the balancing power market, ensuring the importance of such plants in the pathway to a highly renewable energy mix.
Since weather and electricity demand forecasts include uncertainties, the dispatch schedule can suffer modifications when compared to the energy provided. Unmet energy delivery is usually associated with drawbacks in form of penalties or reduced prices. A dispatch optimization algorithm was developed at DLR. It is used to derive a STE plant operation schedule for the upcoming 48 hours. It takes into account weather and electricity pricing forecasts with a special focus in the incorporation of uncertainty information. Its application under different market conditions, according to the country where it would be inserted, is of extreme importance to evaluate the economic benefit of such optimizers.
The tasks to be performed during the master thesis are:
- simulations of STE plant operation based on optimizer schedules with weather forecast for several countries
- analysis of the impact of uncertainties on the dispatch optimization
- assessment of different optimization strategies relevant for the energy market
- technical and economic assessment of the concepts